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Record W2964788883 · doi:10.14195/1647-8606_62-1_3

Coping in the final frontier: An intervention to reduce spaceflight-induced stress

2019· article· en· W2964788883 on OpenAlexaff
Lucas Monzani, Małgorzata W. Kożusznik, Pilar Ripoll, Rolf van Dick, José María Peiró Silla

Bibliographic record

VenuePsychologica · 2019
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsWestern University
Fundersnot available
KeywordsSpaceflightPsychosocialPsychological interventionPsychologyCrewCoping (psychology)Human spaceflightApplied psychologyStressorSocial psychologyEngineeringAeronauticsClinical psychologySpace explorationPsychotherapist

Abstract

fetched live from OpenAlex

Research in human spaceflight has extensively documented how microgravity environments, such as spaceflight across Low Earth Orbit (LEO), affects astronauts’ and Spaceflight Participants’ emotions. However, a more refined understanding of this topic will become especially relevant as national and international space agencies increase the duration of manned space missions, and as the private sector fully enters the aerospace arena. In this paper, we analyze the strengths and weaknesses of the four main types of interventions for dealing with the stressors associated with human spaceflight (i.e., ergonomic, physiological, psychological, and psychosocial), and then elaborate on a psychosocial intervention grounded on evidence-based interventions across several fields of psychological research. Among the components of such interventions, we recommend adopting advanced stress coping strategies, developing emotional and intercultural competencies and crafting a shared social identity among crew members. Our proposed intervention aims to enhance the efficacy of social support as a key coping mechanism and applies to crewmembers and spaceflight participants of diverse cultural backgrounds who, most likely, will work using computer-mediated communication (CMC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.392
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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